A Bayesian State-Space Approach to Mapping Directional Brain Networks

The human brain is a directional network system of brain regions involving directional connectivity. Seizures are a directional network phenomenon as abnormal neuronal activities start from a seizure onset zone (SOZ) and propagate to otherwise healthy regions. To localize the SOZ of an epileptic patient, clinicians use intracranial electroencephalography (iEEG) to record the patient’s intracranial brain activity in many small regions. iEEG data are high-dimensional multivariate time series. We build a state-space multivariate autoregression (SSMAR) for iEEG data to model the underlying directional brain network. To produce scientifically interpretable network results, we incorporate into the SSMAR the scientific knowledge that the underlying brain network tends to have a cluster structure. Specifically, we assign to the SSMAR parameters a stochastic-blockmodel-motivated prior, which reflects the cluster structure. We develop a Bayesian framework to estimate the SSMAR, infer directional connections, and identify clusters for the unobserved network edges. The new method is robust to violations of model assumptions and outperforms existing network methods. By applying the new method to an epileptic patient’s iEEG data, we reveal seizure initiation and propagation in the patient’s directional brain network and discover a unique directional connectivity property of the SOZ. Overall, the network results obtained in this study bring new insights into epileptic patients’ normal and abnormal epileptic brain mechanisms and have the potential to assist neurologists and clinicians in localizing the SOZ—a long-standing research focus in epilepsy diagnosis and treatment. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.